Overview

Dataset statistics

Number of variables16
Number of observations421570
Missing cells1422431
Missing cells (%)21.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory68.0 MiB
Average record size in memory169.1 B

Variable types

Numeric13
Categorical2
Boolean1

Alerts

Date has a high cardinality: 143 distinct valuesHigh cardinality
Store is highly overall correlated with TypeHigh correlation
Size is highly overall correlated with MarkDown5 and 1 other fieldsHigh correlation
MarkDown1 is highly overall correlated with MarkDown4 and 1 other fieldsHigh correlation
MarkDown4 is highly overall correlated with MarkDown1High correlation
MarkDown5 is highly overall correlated with Size and 1 other fieldsHigh correlation
Type is highly overall correlated with Store and 1 other fieldsHigh correlation
IsHoliday is highly imbalanced (63.3%)Imbalance
MarkDown1 has 270889 (64.3%) missing valuesMissing
MarkDown2 has 310322 (73.6%) missing valuesMissing
MarkDown3 has 284479 (67.5%) missing valuesMissing
MarkDown4 has 286603 (68.0%) missing valuesMissing
MarkDown5 has 270138 (64.1%) missing valuesMissing
Date is uniformly distributedUniform

Reproduction

Analysis started2023-06-27 15:06:04.585243
Analysis finished2023-06-27 15:07:07.257395
Duration1 minute and 2.67 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Store
Real number (ℝ)

Distinct45
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.200546
Minimum1
Maximum45
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 MiB
2023-06-27T15:07:07.870462image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q111
median22
Q333
95-th percentile43
Maximum45
Range44
Interquartile range (IQR)22

Descriptive statistics

Standard deviation12.785297
Coefficient of variation (CV)0.57590014
Kurtosis-1.1465028
Mean22.200546
Median Absolute Deviation (MAD)11
Skewness0.077762502
Sum9359084
Variance163.46383
MonotonicityIncreasing
2023-06-27T15:07:08.196523image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
13 10474
 
2.5%
10 10315
 
2.4%
4 10272
 
2.4%
1 10244
 
2.4%
2 10238
 
2.4%
24 10228
 
2.4%
27 10225
 
2.4%
34 10224
 
2.4%
20 10214
 
2.4%
6 10211
 
2.4%
Other values (35) 318925
75.7%
ValueCountFrequency (%)
1 10244
2.4%
2 10238
2.4%
3 9036
2.1%
4 10272
2.4%
5 8999
2.1%
6 10211
2.4%
7 9762
2.3%
8 9895
2.3%
9 8867
2.1%
10 10315
2.4%
ValueCountFrequency (%)
45 9637
2.3%
44 7169
1.7%
43 6751
1.6%
42 6953
1.6%
41 10088
2.4%
40 10017
2.4%
39 9878
2.3%
38 7362
1.7%
37 7206
1.7%
36 6222
1.5%

Dept
Real number (ℝ)

Distinct81
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.260317
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 MiB
2023-06-27T15:07:08.526775image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q118
median37
Q374
95-th percentile95
Maximum99
Range98
Interquartile range (IQR)56

Descriptive statistics

Standard deviation30.492054
Coefficient of variation (CV)0.68892534
Kurtosis-1.2155706
Mean44.260317
Median Absolute Deviation (MAD)23
Skewness0.35822319
Sum18658822
Variance929.76536
MonotonicityNot monotonic
2023-06-27T15:07:08.865312image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 6435
 
1.5%
16 6435
 
1.5%
92 6435
 
1.5%
38 6435
 
1.5%
40 6435
 
1.5%
2 6435
 
1.5%
82 6435
 
1.5%
46 6435
 
1.5%
95 6435
 
1.5%
81 6435
 
1.5%
Other values (71) 357220
84.7%
ValueCountFrequency (%)
1 6435
1.5%
2 6435
1.5%
3 6435
1.5%
4 6435
1.5%
5 6347
1.5%
6 5986
1.4%
7 6435
1.5%
8 6435
1.5%
9 6354
1.5%
10 6435
1.5%
ValueCountFrequency (%)
99 862
 
0.2%
98 5836
1.4%
97 6278
1.5%
96 4854
1.2%
95 6435
1.5%
94 5685
1.3%
93 5913
1.4%
92 6435
1.5%
91 6435
1.5%
90 6435
1.5%

Date
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct143
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.4 MiB
2011-12-23
 
3027
2011-11-25
 
3021
2011-12-16
 
3013
2011-12-09
 
3010
2012-02-17
 
3007
Other values (138)
406492 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters4215700
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2010-02-05
2nd row2010-02-05
3rd row2010-02-05
4th row2010-02-05
5th row2010-02-05

Common Values

ValueCountFrequency (%)
2011-12-23 3027
 
0.7%
2011-11-25 3021
 
0.7%
2011-12-16 3013
 
0.7%
2011-12-09 3010
 
0.7%
2012-02-17 3007
 
0.7%
2011-12-30 3003
 
0.7%
2012-02-10 3001
 
0.7%
2011-12-02 2994
 
0.7%
2012-03-02 2990
 
0.7%
2012-10-12 2990
 
0.7%
Other values (133) 391514
92.9%

Length

2023-06-27T15:07:09.188201image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2011-12-23 3027
 
0.7%
2011-11-25 3021
 
0.7%
2011-12-16 3013
 
0.7%
2011-12-09 3010
 
0.7%
2012-02-17 3007
 
0.7%
2011-12-30 3003
 
0.7%
2012-02-10 3001
 
0.7%
2011-12-02 2994
 
0.7%
2012-03-02 2990
 
0.7%
2012-10-12 2990
 
0.7%
Other values (133) 391514
92.9%

Most occurring characters

ValueCountFrequency (%)
0 1098526
26.1%
1 899707
21.3%
- 843140
20.0%
2 791099
18.8%
3 103408
 
2.5%
4 82539
 
2.0%
6 82450
 
2.0%
7 82241
 
2.0%
9 79610
 
1.9%
5 76564
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3372560
80.0%
Dash Punctuation 843140
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1098526
32.6%
1 899707
26.7%
2 791099
23.5%
3 103408
 
3.1%
4 82539
 
2.4%
6 82450
 
2.4%
7 82241
 
2.4%
9 79610
 
2.4%
5 76564
 
2.3%
8 76416
 
2.3%
Dash Punctuation
ValueCountFrequency (%)
- 843140
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4215700
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1098526
26.1%
1 899707
21.3%
- 843140
20.0%
2 791099
18.8%
3 103408
 
2.5%
4 82539
 
2.0%
6 82450
 
2.0%
7 82241
 
2.0%
9 79610
 
1.9%
5 76564
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4215700
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1098526
26.1%
1 899707
21.3%
- 843140
20.0%
2 791099
18.8%
3 103408
 
2.5%
4 82539
 
2.0%
6 82450
 
2.0%
7 82241
 
2.0%
9 79610
 
1.9%
5 76564
 
1.8%

Weekly_Sales
Real number (ℝ)

Distinct359464
Distinct (%)85.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15981.258
Minimum-4988.94
Maximum693099.36
Zeros73
Zeros (%)< 0.1%
Negative1285
Negative (%)0.3%
Memory size6.4 MiB
2023-06-27T15:07:09.476943image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-4988.94
5-th percentile59.9745
Q12079.65
median7612.03
Q320205.853
95-th percentile61201.951
Maximum693099.36
Range698088.3
Interquartile range (IQR)18126.202

Descriptive statistics

Standard deviation22711.184
Coefficient of variation (CV)1.4211136
Kurtosis21.49129
Mean15981.258
Median Absolute Deviation (MAD)6747.645
Skewness3.2620082
Sum6.737219 × 109
Variance5.1579786 × 108
MonotonicityNot monotonic
2023-06-27T15:07:09.800004image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 353
 
0.1%
5 289
 
0.1%
20 232
 
0.1%
15 215
 
0.1%
12 175
 
< 0.1%
1 169
 
< 0.1%
10.47 167
 
< 0.1%
11.97 154
 
< 0.1%
2 148
 
< 0.1%
7 146
 
< 0.1%
Other values (359454) 419522
99.5%
ValueCountFrequency (%)
-4988.94 1
 
< 0.1%
-3924 1
 
< 0.1%
-1750 1
 
< 0.1%
-1699 1
 
< 0.1%
-1321.48 1
 
< 0.1%
-1098 3
< 0.1%
-1008.96 1
 
< 0.1%
-898 1
 
< 0.1%
-863 1
 
< 0.1%
-798 4
< 0.1%
ValueCountFrequency (%)
693099.36 1
< 0.1%
649770.18 1
< 0.1%
630999.19 1
< 0.1%
627962.93 1
< 0.1%
474330.1 1
< 0.1%
422306.25 1
< 0.1%
420586.57 1
< 0.1%
406988.63 1
< 0.1%
404245.03 1
< 0.1%
393705.2 1
< 0.1%

IsHoliday
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.6 MiB
False
391909 
True
 
29661
ValueCountFrequency (%)
False 391909
93.0%
True 29661
 
7.0%
2023-06-27T15:07:10.148508image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Type
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.4 MiB
A
215478 
B
163495 
C
42597 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters421570
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 215478
51.1%
B 163495
38.8%
C 42597
 
10.1%

Length

2023-06-27T15:07:10.404029image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-27T15:07:10.701223image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
a 215478
51.1%
b 163495
38.8%
c 42597
 
10.1%

Most occurring characters

ValueCountFrequency (%)
A 215478
51.1%
B 163495
38.8%
C 42597
 
10.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 421570
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 215478
51.1%
B 163495
38.8%
C 42597
 
10.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 421570
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 215478
51.1%
B 163495
38.8%
C 42597
 
10.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 421570
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 215478
51.1%
B 163495
38.8%
C 42597
 
10.1%

Size
Real number (ℝ)

Distinct40
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean136727.92
Minimum34875
Maximum219622
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 MiB
2023-06-27T15:07:10.978105image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum34875
5-th percentile39690
Q193638
median140167
Q3202505
95-th percentile206302
Maximum219622
Range184747
Interquartile range (IQR)108867

Descriptive statistics

Standard deviation60980.583
Coefficient of variation (CV)0.44599951
Kurtosis-1.2063459
Mean136727.92
Median Absolute Deviation (MAD)62140
Skewness-0.32584977
Sum5.7640387 × 1010
Variance3.7186315 × 109
MonotonicityNot monotonic
2023-06-27T15:07:11.286935image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
39690 20802
 
4.9%
39910 20597
 
4.9%
203819 20376
 
4.8%
219622 10474
 
2.5%
126512 10315
 
2.4%
205863 10272
 
2.4%
151315 10244
 
2.4%
202307 10238
 
2.4%
204184 10225
 
2.4%
158114 10224
 
2.4%
Other values (30) 287803
68.3%
ValueCountFrequency (%)
34875 8999
2.1%
37392 9036
2.1%
39690 20802
4.9%
39910 20597
4.9%
41062 6751
 
1.6%
42988 7156
 
1.7%
57197 9443
2.2%
70713 9762
2.3%
93188 9864
2.3%
93638 9455
2.2%
ValueCountFrequency (%)
219622 10474
2.5%
207499 10062
2.4%
206302 10113
2.4%
205863 10272
2.4%
204184 10225
2.4%
203819 20376
4.8%
203750 10142
2.4%
203742 10214
2.4%
203007 10202
2.4%
202505 10211
2.4%

Temperature
Real number (ℝ)

Distinct3528
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60.090059
Minimum-2.06
Maximum100.14
Zeros0
Zeros (%)0.0%
Negative69
Negative (%)< 0.1%
Memory size6.4 MiB
2023-06-27T15:07:11.619258image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-2.06
5-th percentile27.31
Q146.68
median62.09
Q374.28
95-th percentile87.27
Maximum100.14
Range102.2
Interquartile range (IQR)27.6

Descriptive statistics

Standard deviation18.447931
Coefficient of variation (CV)0.30700471
Kurtosis-0.63592198
Mean60.090059
Median Absolute Deviation (MAD)13.63
Skewness-0.32140415
Sum25332166
Variance340.32616
MonotonicityNot monotonic
2023-06-27T15:07:11.942495image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50.43 709
 
0.2%
67.87 646
 
0.2%
72.62 594
 
0.1%
76.67 583
 
0.1%
70.28 563
 
0.1%
76.03 555
 
0.1%
50.56 544
 
0.1%
64.05 542
 
0.1%
64.21 519
 
0.1%
50.81 487
 
0.1%
Other values (3518) 415828
98.6%
ValueCountFrequency (%)
-2.06 69
< 0.1%
5.54 68
< 0.1%
6.23 69
< 0.1%
7.46 69
< 0.1%
9.51 70
< 0.1%
9.55 69
< 0.1%
10.09 66
< 0.1%
10.11 68
< 0.1%
10.24 69
< 0.1%
10.53 72
< 0.1%
ValueCountFrequency (%)
100.14 44
 
< 0.1%
100.07 46
 
< 0.1%
99.66 48
 
< 0.1%
99.22 185
< 0.1%
99.2 46
 
< 0.1%
98.43 43
 
< 0.1%
98.15 47
 
< 0.1%
97.66 42
 
< 0.1%
97.6 48
 
< 0.1%
97.18 187
< 0.1%

Fuel_Price
Real number (ℝ)

Distinct892
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3610265
Minimum2.472
Maximum4.468
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 MiB
2023-06-27T15:07:12.269256image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2.472
5-th percentile2.653
Q12.933
median3.452
Q33.738
95-th percentile4.029
Maximum4.468
Range1.996
Interquartile range (IQR)0.805

Descriptive statistics

Standard deviation0.45851454
Coefficient of variation (CV)0.13642098
Kurtosis-1.1854045
Mean3.3610265
Median Absolute Deviation (MAD)0.375
Skewness-0.1049015
Sum1416908
Variance0.21023558
MonotonicityNot monotonic
2023-06-27T15:07:12.613601image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.638 2548
 
0.6%
3.63 2164
 
0.5%
2.771 1917
 
0.5%
3.891 1856
 
0.4%
3.594 1796
 
0.4%
3.524 1793
 
0.4%
3.523 1792
 
0.4%
2.72 1790
 
0.4%
3.666 1778
 
0.4%
2.78 1656
 
0.4%
Other values (882) 402480
95.5%
ValueCountFrequency (%)
2.472 38
 
< 0.1%
2.513 45
 
< 0.1%
2.514 906
0.2%
2.52 39
 
< 0.1%
2.533 42
 
< 0.1%
2.539 37
 
< 0.1%
2.54 147
 
< 0.1%
2.542 45
 
< 0.1%
2.545 38
 
< 0.1%
2.548 902
0.2%
ValueCountFrequency (%)
4.468 368
0.1%
4.449 358
0.1%
4.308 168
< 0.1%
4.301 360
0.1%
4.294 363
0.1%
4.293 192
< 0.1%
4.288 172
< 0.1%
4.282 173
< 0.1%
4.277 357
0.1%
4.273 366
0.1%

MarkDown1
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct2277
Distinct (%)1.5%
Missing270889
Missing (%)64.3%
Infinite0
Infinite (%)0.0%
Mean7246.4202
Minimum0.27
Maximum88646.76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 MiB
2023-06-27T15:07:12.968752image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.27
5-th percentile149.19
Q12240.27
median5347.45
Q39210.9
95-th percentile21801.35
Maximum88646.76
Range88646.49
Interquartile range (IQR)6970.63

Descriptive statistics

Standard deviation8291.2213
Coefficient of variation (CV)1.1441817
Kurtosis17.606263
Mean7246.4202
Median Absolute Deviation (MAD)3430.74
Skewness3.3418447
Sum1.0918978 × 109
Variance68744351
MonotonicityNot monotonic
2023-06-27T15:07:13.280308image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.5 102
 
< 0.1%
460.73 102
 
< 0.1%
175.64 93
 
< 0.1%
1282.42 75
 
< 0.1%
9264.48 75
 
< 0.1%
686.24 75
 
< 0.1%
5924.71 75
 
< 0.1%
1483.17 75
 
< 0.1%
3124.45 74
 
< 0.1%
6809.96 74
 
< 0.1%
Other values (2267) 149861
35.5%
(Missing) 270889
64.3%
ValueCountFrequency (%)
0.27 51
< 0.1%
0.5 49
< 0.1%
1.5 102
< 0.1%
1.94 50
< 0.1%
2.12 52
< 0.1%
2.4 49
< 0.1%
2.42 50
< 0.1%
2.43 51
< 0.1%
2.8 50
< 0.1%
2.91 51
< 0.1%
ValueCountFrequency (%)
88646.76 68
< 0.1%
78124.5 70
< 0.1%
75149.79 73
< 0.1%
65021.23 73
< 0.1%
62567.6 66
< 0.1%
62172.73 72
< 0.1%
60740.64 70
< 0.1%
60394.73 72
< 0.1%
58928.52 72
< 0.1%
56917.7 71
< 0.1%

MarkDown2
Real number (ℝ)

Distinct1499
Distinct (%)1.3%
Missing310322
Missing (%)73.6%
Infinite0
Infinite (%)0.0%
Mean3334.6286
Minimum-265.76
Maximum104519.54
Zeros207
Zeros (%)< 0.1%
Negative1311
Negative (%)0.3%
Memory size6.4 MiB
2023-06-27T15:07:13.596981image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-265.76
5-th percentile1.95
Q141.6
median192
Q31926.94
95-th percentile16497.47
Maximum104519.54
Range104785.3
Interquartile range (IQR)1885.34

Descriptive statistics

Standard deviation9475.3573
Coefficient of variation (CV)2.841503
Kurtosis37.589561
Mean3334.6286
Median Absolute Deviation (MAD)184.73
Skewness5.4412612
Sum3.7097076 × 108
Variance89782396
MonotonicityNot monotonic
2023-06-27T15:07:13.916664image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.91 539
 
0.1%
3 493
 
0.1%
0.5 485
 
0.1%
1.5 471
 
0.1%
4 367
 
0.1%
6 365
 
0.1%
7.64 354
 
0.1%
3.82 353
 
0.1%
19 345
 
0.1%
5.73 345
 
0.1%
Other values (1489) 107131
 
25.4%
(Missing) 310322
73.6%
ValueCountFrequency (%)
-265.76 71
< 0.1%
-192 72
< 0.1%
-20 72
< 0.1%
-10.98 60
< 0.1%
-10.5 143
< 0.1%
-9.98 68
< 0.1%
-9.94 62
< 0.1%
-7.6 69
< 0.1%
-7.01 69
< 0.1%
-6.69 69
< 0.1%
ValueCountFrequency (%)
104519.54 72
< 0.1%
97740.99 73
< 0.1%
92523.94 73
< 0.1%
89121.94 74
< 0.1%
82881.16 73
< 0.1%
72413.71 72
< 0.1%
70574.85 71
< 0.1%
58804.91 69
< 0.1%
58046.41 71
< 0.1%
56106.2 72
< 0.1%

MarkDown3
Real number (ℝ)

Distinct1662
Distinct (%)1.2%
Missing284479
Missing (%)67.5%
Infinite0
Infinite (%)0.0%
Mean1439.4214
Minimum-29.1
Maximum141630.61
Zeros67
Zeros (%)< 0.1%
Negative257
Negative (%)0.1%
Memory size6.4 MiB
2023-06-27T15:07:14.227156image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-29.1
5-th percentile0.65
Q15.08
median24.6
Q3103.99
95-th percentile1059.9
Maximum141630.61
Range141659.71
Interquartile range (IQR)98.91

Descriptive statistics

Standard deviation9623.0783
Coefficient of variation (CV)6.6853796
Kurtosis77.687772
Mean1439.4214
Median Absolute Deviation (MAD)22.6
Skewness8.399453
Sum1.9733172 × 108
Variance92603636
MonotonicityNot monotonic
2023-06-27T15:07:14.543329image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 754
 
0.2%
6 710
 
0.2%
2 660
 
0.2%
1 611
 
0.1%
0.22 487
 
0.1%
0.5 463
 
0.1%
0.01 444
 
0.1%
4 439
 
0.1%
3.2 379
 
0.1%
1.98 363
 
0.1%
Other values (1652) 131781
31.3%
(Missing) 284479
67.5%
ValueCountFrequency (%)
-29.1 72
 
< 0.1%
-1 70
 
< 0.1%
-0.87 46
 
< 0.1%
-0.2 69
 
< 0.1%
0 67
 
< 0.1%
0.01 444
0.1%
0.02 124
 
< 0.1%
0.04 241
0.1%
0.05 71
 
< 0.1%
0.06 205
< 0.1%
ValueCountFrequency (%)
141630.61 74
< 0.1%
109030.75 75
< 0.1%
103991.94 72
< 0.1%
101378.79 73
< 0.1%
89402.64 71
< 0.1%
88805.58 72
< 0.1%
83340.33 74
< 0.1%
83192.81 74
< 0.1%
79621.2 72
< 0.1%
77451.26 73
< 0.1%

MarkDown4
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1944
Distinct (%)1.4%
Missing286603
Missing (%)68.0%
Infinite0
Infinite (%)0.0%
Mean3383.1683
Minimum0.22
Maximum67474.85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 MiB
2023-06-27T15:07:14.875383image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.22
5-th percentile28.76
Q1504.22
median1481.31
Q33595.04
95-th percentile12645.96
Maximum67474.85
Range67474.63
Interquartile range (IQR)3090.82

Descriptive statistics

Standard deviation6292.384
Coefficient of variation (CV)1.8599087
Kurtosis29.996815
Mean3383.1683
Median Absolute Deviation (MAD)1167.55
Skewness4.8475
Sum4.5661607 × 108
Variance39594097
MonotonicityNot monotonic
2023-06-27T15:07:15.197353image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9 280
 
0.1%
4 200
 
< 0.1%
2 197
 
< 0.1%
3 146
 
< 0.1%
47 143
 
< 0.1%
67.72 142
 
< 0.1%
657.56 141
 
< 0.1%
17 141
 
< 0.1%
8 140
 
< 0.1%
1330.36 140
 
< 0.1%
Other values (1934) 133297
31.6%
(Missing) 286603
68.0%
ValueCountFrequency (%)
0.22 57
 
< 0.1%
0.41 52
 
< 0.1%
0.46 48
 
< 0.1%
0.78 52
 
< 0.1%
0.87 49
 
< 0.1%
0.92 45
 
< 0.1%
1.5 55
 
< 0.1%
1.88 48
 
< 0.1%
1.98 44
 
< 0.1%
2 197
< 0.1%
ValueCountFrequency (%)
67474.85 72
< 0.1%
57817.56 74
< 0.1%
57815.43 68
< 0.1%
53603.99 72
< 0.1%
52739.02 72
< 0.1%
48403.53 70
< 0.1%
48159.86 73
< 0.1%
48086.64 72
< 0.1%
47452.43 73
< 0.1%
46238.28 71
< 0.1%

MarkDown5
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct2293
Distinct (%)1.5%
Missing270138
Missing (%)64.1%
Infinite0
Infinite (%)0.0%
Mean4628.9751
Minimum135.16
Maximum108519.28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 MiB
2023-06-27T15:07:15.538946image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum135.16
5-th percentile715.52
Q11878.44
median3359.45
Q35563.8
95-th percentile11269.24
Maximum108519.28
Range108384.12
Interquartile range (IQR)3685.36

Descriptive statistics

Standard deviation5962.8875
Coefficient of variation (CV)1.2881658
Kurtosis107.84927
Mean4628.9751
Median Absolute Deviation (MAD)1702.47
Skewness8.1699095
Sum7.0097495 × 108
Variance35556027
MonotonicityNot monotonic
2023-06-27T15:07:15.863403image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2743.18 136
 
< 0.1%
1064.56 120
 
< 0.1%
9083.54 75
 
< 0.1%
3567.03 75
 
< 0.1%
3557.67 75
 
< 0.1%
20371.02 75
 
< 0.1%
4180.29 75
 
< 0.1%
1773.53 74
 
< 0.1%
3932.94 74
 
< 0.1%
4464.45 74
 
< 0.1%
Other values (2283) 150579
35.7%
(Missing) 270138
64.1%
ValueCountFrequency (%)
135.16 65
< 0.1%
153.04 47
< 0.1%
153.9 49
< 0.1%
164.08 52
< 0.1%
170.64 69
< 0.1%
171.76 71
< 0.1%
180.07 64
< 0.1%
212.75 50
< 0.1%
224.86 50
< 0.1%
227.12 48
< 0.1%
ValueCountFrequency (%)
108519.28 68
< 0.1%
105223.11 70
< 0.1%
85851.87 68
< 0.1%
63005.58 69
< 0.1%
58068.14 69
< 0.1%
57029.78 68
< 0.1%
53212.72 70
< 0.1%
37581.27 70
< 0.1%
36430.33 71
< 0.1%
36360.42 72
< 0.1%

CPI
Real number (ℝ)

Distinct2145
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean171.20195
Minimum126.064
Maximum227.23281
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 MiB
2023-06-27T15:07:16.186792image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum126.064
5-th percentile126.49626
Q1132.02267
median182.31878
Q3212.41699
95-th percentile221.94156
Maximum227.23281
Range101.16881
Interquartile range (IQR)80.394326

Descriptive statistics

Standard deviation39.159276
Coefficient of variation (CV)0.22873149
Kurtosis-1.8297144
Mean171.20195
Median Absolute Deviation (MAD)41.434863
Skewness0.085219285
Sum72173605
Variance1533.4489
MonotonicityNot monotonic
2023-06-27T15:07:16.512439image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
129.8555333 711
 
0.2%
131.1083333 708
 
0.2%
129.8459667 707
 
0.2%
130.3849032 706
 
0.2%
130.6457931 706
 
0.2%
131.0756667 706
 
0.2%
130.683 706
 
0.2%
130.4546207 705
 
0.2%
130.7196333 705
 
0.2%
130.737871 704
 
0.2%
Other values (2135) 414506
98.3%
ValueCountFrequency (%)
126.064 678
0.2%
126.0766452 679
0.2%
126.0854516 675
0.2%
126.0892903 682
0.2%
126.1019355 686
0.2%
126.1069032 681
0.2%
126.1119032 682
0.2%
126.114 687
0.2%
126.1145806 689
0.2%
126.1266 683
0.2%
ValueCountFrequency (%)
227.2328068 63
< 0.1%
227.214288 62
< 0.1%
227.1693919 63
< 0.1%
227.0369359 70
< 0.1%
227.0184166 69
< 0.1%
226.9873637 134
< 0.1%
226.9735448 69
< 0.1%
226.9688442 134
< 0.1%
226.9662325 63
< 0.1%
226.9239785 135
< 0.1%

Unemployment
Real number (ℝ)

Distinct349
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.9602887
Minimum3.879
Maximum14.313
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 MiB
2023-06-27T15:07:16.829985image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum3.879
5-th percentile5.326
Q16.891
median7.866
Q38.572
95-th percentile12.187
Maximum14.313
Range10.434
Interquartile range (IQR)1.681

Descriptive statistics

Standard deviation1.863296
Coefficient of variation (CV)0.23407393
Kurtosis2.7312166
Mean7.9602887
Median Absolute Deviation (MAD)0.858
Skewness1.1837426
Sum3355818.9
Variance3.4718721
MonotonicityNot monotonic
2023-06-27T15:07:17.153556image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.099 5152
 
1.2%
8.163 3636
 
0.9%
7.852 3614
 
0.9%
7.343 3416
 
0.8%
7.057 3414
 
0.8%
7.931 3400
 
0.8%
7.441 3397
 
0.8%
6.565 3370
 
0.8%
8.2 3361
 
0.8%
6.891 3360
 
0.8%
Other values (339) 385450
91.4%
ValueCountFrequency (%)
3.879 287
 
0.1%
4.077 938
0.2%
4.125 1831
0.4%
4.145 562
 
0.1%
4.156 1815
0.4%
4.261 1829
0.4%
4.308 935
0.2%
4.42 1855
0.4%
4.584 1988
0.5%
4.607 935
0.2%
ValueCountFrequency (%)
14.313 2636
0.6%
14.18 2423
0.6%
14.099 2441
0.6%
14.021 2263
0.5%
13.975 1529
0.4%
13.736 2464
0.6%
13.503 2661
0.6%
12.89 2491
0.6%
12.187 2507
0.6%
11.627 2502
0.6%

Interactions

2023-06-27T15:07:00.924106image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:18.403790image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:22.266879image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:26.468758image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:30.443569image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:34.490316image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:38.453601image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:42.420705image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:45.972084image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:49.519733image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:53.075233image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:55.653374image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:58.296814image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:07:01.123189image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:18.703107image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:22.587562image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:26.765243image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:30.740566image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:34.760555image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:38.755139image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:42.683233image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:46.265009image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:49.794365image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:53.268356image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:55.841554image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:58.489448image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:07:01.329638image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:18.993830image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:22.878644image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:27.105682image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:31.080478image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:35.059802image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:39.105132image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:42.954808image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:46.507023image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:50.073912image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:53.466382image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:56.030201image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:58.690341image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:07:01.554125image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:19.305827image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:23.229150image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:27.424430image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:31.436280image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:35.368525image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:39.429696image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:43.235671image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:46.788376image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:50.367479image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:53.671720image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:56.258908image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:58.911763image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:07:01.812774image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:19.620225image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:23.577809image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:27.782846image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:31.749872image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:35.707475image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:39.792637image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:43.521299image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:47.078440image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:50.661124image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:53.886073image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:56.545682image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:59.166893image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:07:02.022568image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:19.917721image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:23.875211image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:28.089673image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:32.094836image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:35.946795image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:40.097744image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:43.788166image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:47.347567image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:50.941115image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:54.077909image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:56.729265image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:59.368933image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:07:02.241760image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:20.222466image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:24.418795image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:28.397049image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:32.427035image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:36.165431image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:40.399756image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:44.074750image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:47.625983image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:51.237748image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:54.290182image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:56.932240image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:59.573643image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:07:02.417412image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:20.490571image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:24.683509image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:28.665458image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:32.700600image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:36.430655image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:40.670577image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:44.333856image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:47.884883image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:51.785571image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:54.471241image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:57.108798image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:59.744148image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:07:02.600114image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:20.769376image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:24.960332image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:28.944945image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:32.984847image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:36.980901image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:40.945101image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:44.601092image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:48.150154image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:51.996490image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:54.664776image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:57.293468image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:59.925670image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:07:02.797400image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:21.064799image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:25.247497image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:29.234270image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:33.287009image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:37.267566image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:41.237193image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:44.880423image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:48.434388image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:52.278940image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:54.871602image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:57.498475image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:07:00.120012image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:07:02.997971image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:21.369407image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:25.540177image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:29.526124image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:33.589701image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:37.557245image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:41.526024image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:45.166389image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:48.717282image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:52.496363image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:55.077927image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:57.698942image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:07:00.316195image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:07:03.209976image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:21.680073image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:25.840142image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:29.834785image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:33.917043image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:37.860106image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:41.830171image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:45.436364image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:48.986403image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:52.692709image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:55.270798image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:57.885812image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:07:00.521457image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:07:03.408966image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:21.965389image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:26.169062image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:30.134627image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:34.213142image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:38.154606image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:42.128568image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:45.705140image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:49.248139image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:52.887343image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:55.459901image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:06:58.070029image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-27T15:07:00.719605image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-06-27T15:07:17.429836image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
StoreDeptWeekly_SalesSizeTemperatureFuel_PriceMarkDown1MarkDown2MarkDown3MarkDown4MarkDown5CPIUnemploymentIsHolidayType
Store1.0000.014-0.102-0.160-0.0570.074-0.2120.009-0.065-0.039-0.156-0.2300.2950.0000.538
Dept0.0141.000-0.0140.0110.0010.0030.0020.0030.0060.0070.006-0.0090.0060.0000.080
Weekly_Sales-0.102-0.0141.0000.290-0.0200.0020.1920.0320.1350.1120.208-0.023-0.0160.0310.089
Size-0.1600.0110.2901.000-0.0430.0040.4990.1490.3000.2880.579-0.005-0.0660.0000.851
Temperature-0.0570.001-0.020-0.0431.0000.1280.002-0.462-0.2570.141-0.0710.1730.0300.1860.123
Fuel_Price0.0740.0030.0020.0040.1281.0000.163-0.155-0.2180.073-0.088-0.041-0.0600.1360.088
MarkDown1-0.2120.0020.1920.4990.0020.1631.0000.2060.1540.7590.508-0.0170.0640.0570.172
MarkDown20.0090.0030.0320.149-0.462-0.1550.2061.0000.0660.1160.152-0.0990.0600.3590.066
MarkDown3-0.0650.0060.1350.300-0.257-0.2180.1540.0661.0000.0020.244-0.1110.0430.4580.065
MarkDown4-0.0390.0070.1120.2880.1410.0730.7590.1160.0021.0000.380-0.0630.0380.1150.066
MarkDown5-0.1560.0060.2080.579-0.071-0.0880.5080.1520.2440.3801.0000.021-0.0190.0600.094
CPI-0.230-0.009-0.023-0.0050.173-0.041-0.017-0.099-0.111-0.0630.0211.000-0.3830.0120.183
Unemployment0.2950.006-0.016-0.0660.030-0.0600.0640.0600.0430.038-0.019-0.3831.0000.0350.181
IsHoliday0.0000.0000.0310.0000.1860.1360.0570.3590.4580.1150.0600.0120.0351.0000.000
Type0.5380.0800.0890.8510.1230.0880.1720.0660.0650.0660.0940.1830.1810.0001.000

Missing values

2023-06-27T15:07:03.816679image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-06-27T15:07:05.009513image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-06-27T15:07:06.728109image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

StoreDeptDateWeekly_SalesIsHolidayTypeSizeTemperatureFuel_PriceMarkDown1MarkDown2MarkDown3MarkDown4MarkDown5CPIUnemployment
0112010-02-0524924.50FalseA15131542.312.572NaNNaNNaNNaNNaN211.0963588.106
1122010-02-0550605.27FalseA15131542.312.572NaNNaNNaNNaNNaN211.0963588.106
2132010-02-0513740.12FalseA15131542.312.572NaNNaNNaNNaNNaN211.0963588.106
3142010-02-0539954.04FalseA15131542.312.572NaNNaNNaNNaNNaN211.0963588.106
4152010-02-0532229.38FalseA15131542.312.572NaNNaNNaNNaNNaN211.0963588.106
5162010-02-055749.03FalseA15131542.312.572NaNNaNNaNNaNNaN211.0963588.106
6172010-02-0521084.08FalseA15131542.312.572NaNNaNNaNNaNNaN211.0963588.106
7182010-02-0540129.01FalseA15131542.312.572NaNNaNNaNNaNNaN211.0963588.106
8192010-02-0516930.99FalseA15131542.312.572NaNNaNNaNNaNNaN211.0963588.106
91102010-02-0530721.50FalseA15131542.312.572NaNNaNNaNNaNNaN211.0963588.106
StoreDeptDateWeekly_SalesIsHolidayTypeSizeTemperatureFuel_PriceMarkDown1MarkDown2MarkDown3MarkDown4MarkDown5CPIUnemployment
42156045852012-10-261689.10FalseB11822158.853.8824018.9158.08100.0211.94858.33192.3088998.667
42156145872012-10-268187.66FalseB11822158.853.8824018.9158.08100.0211.94858.33192.3088998.667
42156245902012-10-2625352.32FalseB11822158.853.8824018.9158.08100.0211.94858.33192.3088998.667
42156345912012-10-2616330.84FalseB11822158.853.8824018.9158.08100.0211.94858.33192.3088998.667
42156445922012-10-2654608.75FalseB11822158.853.8824018.9158.08100.0211.94858.33192.3088998.667
42156545932012-10-262487.80FalseB11822158.853.8824018.9158.08100.0211.94858.33192.3088998.667
42156645942012-10-265203.31FalseB11822158.853.8824018.9158.08100.0211.94858.33192.3088998.667
42156745952012-10-2656017.47FalseB11822158.853.8824018.9158.08100.0211.94858.33192.3088998.667
42156845972012-10-266817.48FalseB11822158.853.8824018.9158.08100.0211.94858.33192.3088998.667
42156945982012-10-261076.80FalseB11822158.853.8824018.9158.08100.0211.94858.33192.3088998.667